Information searching utilizing the Internet is a widely growing practice among people young and old alike. Generally, a person wishing to gain knowledge about (or otherwise having an interest in) a particular topic or thing navigates to an online search engine and inputs a query into an appropriate query-input field. A search then is initiated on the query and a selection of search results relevant to the input query is presented for inspection by and/or enjoyment of the user. With the widespread proliferation of such query-based search practices, methods and systems for locating and presenting relevant information in response to input queries have become increasingly important. For instance, search engines desiring to become users' go-to resource for conducting on-line searches are continually refining the systems and methods they utilize to determine the relevance of potential search results to an input search query.
Another growing trend in online information searching is the search for online images in addition to, or instead of, text-based documents. Two primary methods of image search have become widely utilized. In a first method, a user inputs one or more textual keywords and images relevant to the keywords are presented in response. For instance, a user inputting the textual keyword “dog” may be presented with a plurality of images showing pictures of dogs upon conducting the keyword search. In a second method, images are utilized as the input query, for instance, when a user desires to view similar images and/or examine textual information regarding what is depicted in the input image. By way of example, a user inputting an image of “Starry Night” by Vincent Van Gogh may be presented with images of other works by Vincent Van Gogh and/or text-based information about the artist, the painting, and the like upon execution of the image search.
Whether presenting images in response to a text-based query or an image-based query, determining the relevance of particular images with respect to the queried information can be an arduous task. In some instances, users may manually associate keywords with images included in an image database and/or keywords may be extracted from information obtained in conjunction with and/or in proximity to an image. Such keywords may then be associated with the image as a keyword tag. Subsequently, when a user is searching for images utilizing a keyword that is at least similar to a keyword tag associated with a given image, the given image may be presented in response to the query. Similarly, an input image having a keyword associated with it as a keyword tag, when searched, may prompt return of other images similarly having the keyword (or a similar keyword) associated therewith as a keyword tag. Tag-based methods and systems such as these, however, often present images only tangentially related to a given query, particularly in instances where the keyword tags associated with an image are varied due to multiple items being depicted in an image.
In other instances, search systems may extract visual characteristics from a given image and attempt to match such characteristics to similar visual characteristics of images in an image database for presentation to the user in response to a query. Visual-characteristic-based methods and systems such as these similarly can present images that are only tangentially related to a given query, for instance, due to the wealth of visual information that may be included in an image, only a portion of which may be related to the input query.
The above-stated challenges are even more acute for dense images, that is, images having a high resolution or otherwise having a lot of associated information. In such instances, keywords or visual characteristics often are associated only with a small portion of an image which can lead to images in which keyword and/or visual characteristic information is less accurate than for images having relatively less associated information.